import gym
import time
import ctypes
import numpy as np
from collections import OrderedDict
from multiprocessing.context import Process
from multiprocessing import Array, Pipe, connection
from typing import Any, List, Tuple, Union, Callable, Optional
from tianshou.env.worker import EnvWorker
from tianshou.env.utils import CloudpickleWrapper
_NP_TO_CT = {
np.bool: ctypes.c_bool,
np.bool_: ctypes.c_bool,
np.uint8: ctypes.c_uint8,
np.uint16: ctypes.c_uint16,
np.uint32: ctypes.c_uint32,
np.uint64: ctypes.c_uint64,
np.int8: ctypes.c_int8,
np.int16: ctypes.c_int16,
np.int32: ctypes.c_int32,
np.int64: ctypes.c_int64,
np.float32: ctypes.c_float,
np.float64: ctypes.c_double,
}
class ShArray:
"""Wrapper of multiprocessing Array."""
def __init__(self, dtype: np.generic, shape: Tuple[int]) -> None:
self.arr = Array(
_NP_TO_CT[dtype.type],
int(np.prod(shape)),
)
self.dtype = dtype
self.shape = shape
def save(self, ndarray: np.ndarray) -> None:
assert isinstance(ndarray, np.ndarray)
dst = self.arr.get_obj()
dst_np = np.frombuffer(dst, dtype=self.dtype).reshape(self.shape)
np.copyto(dst_np, ndarray)
def get(self) -> np.ndarray:
obj = self.arr.get_obj()
return np.frombuffer(obj, dtype=self.dtype).reshape(self.shape)
def _setup_buf(space: gym.Space) -> Union[dict, tuple, ShArray]:
if isinstance(space, gym.spaces.Dict):
assert isinstance(space.spaces, OrderedDict)
return {k: _setup_buf(v) for k, v in space.spaces.items()}
elif isinstance(space, gym.spaces.Tuple):
assert isinstance(space.spaces, tuple)
return tuple([_setup_buf(t) for t in space.spaces])
else:
return ShArray(space.dtype, space.shape)
def _worker(
parent: connection.Connection,
p: connection.Connection,
env_fn_wrapper: CloudpickleWrapper,
obs_bufs: Optional[Union[dict, tuple, ShArray]] = None,
) -> None:
def _encode_obs(
obs: Union[dict, tuple, np.ndarray],
buffer: Union[dict, tuple, ShArray],
) -> None:
if isinstance(obs, np.ndarray) and isinstance(buffer, ShArray):
buffer.save(obs)
elif isinstance(obs, tuple) and isinstance(buffer, tuple):
for o, b in zip(obs, buffer):
_encode_obs(o, b)
elif isinstance(obs, dict) and isinstance(buffer, dict):
for k in obs.keys():
_encode_obs(obs[k], buffer[k])
return None
parent.close()
env = env_fn_wrapper.data()
try:
while True:
try:
cmd, data = p.recv()
except EOFError: # the pipe has been closed
p.close()
break
if cmd == "step":
obs, reward, done, info = env.step(data)
if obs_bufs is not None:
_encode_obs(obs, obs_bufs)
obs = None
p.send((obs, reward, done, info))
elif cmd == "reset":
obs = env.reset()
if obs_bufs is not None:
_encode_obs(obs, obs_bufs)
obs = None
p.send(obs)
elif cmd == "close":
p.send(env.close())
p.close()
break
elif cmd == "render":
p.send(env.render(**data) if hasattr(env, "render") else None)
elif cmd == "seed":
p.send(env.seed(data) if hasattr(env, "seed") else None)
elif cmd == "getattr":
p.send(getattr(env, data) if hasattr(env, data) else None)
else:
p.close()
raise NotImplementedError
except KeyboardInterrupt:
p.close()
[docs]class SubprocEnvWorker(EnvWorker):
"""Subprocess worker used in SubprocVectorEnv and ShmemVectorEnv."""
def __init__(
self, env_fn: Callable[[], gym.Env], share_memory: bool = False
) -> None:
super().__init__(env_fn)
self.parent_remote, self.child_remote = Pipe()
self.share_memory = share_memory
self.buffer: Optional[Union[dict, tuple, ShArray]] = None
if self.share_memory:
dummy = env_fn()
obs_space = dummy.observation_space
dummy.close()
del dummy
self.buffer = _setup_buf(obs_space)
args = (
self.parent_remote,
self.child_remote,
CloudpickleWrapper(env_fn),
self.buffer,
)
self.process = Process(target=_worker, args=args, daemon=True)
self.process.start()
self.child_remote.close()
def __getattr__(self, key: str) -> Any:
self.parent_remote.send(["getattr", key])
return self.parent_remote.recv()
def _decode_obs(self) -> Union[dict, tuple, np.ndarray]:
def decode_obs(
buffer: Optional[Union[dict, tuple, ShArray]]
) -> Union[dict, tuple, np.ndarray]:
if isinstance(buffer, ShArray):
return buffer.get()
elif isinstance(buffer, tuple):
return tuple([decode_obs(b) for b in buffer])
elif isinstance(buffer, dict):
return {k: decode_obs(v) for k, v in buffer.items()}
else:
raise NotImplementedError
return decode_obs(self.buffer)
[docs] def reset(self) -> Any:
self.parent_remote.send(["reset", None])
obs = self.parent_remote.recv()
if self.share_memory:
obs = self._decode_obs()
return obs
[docs] @staticmethod
def wait( # type: ignore
workers: List["SubprocEnvWorker"],
wait_num: int,
timeout: Optional[float] = None,
) -> List["SubprocEnvWorker"]:
remain_conns = conns = [x.parent_remote for x in workers]
ready_conns: List[connection.Connection] = []
remain_time, t1 = timeout, time.time()
while len(remain_conns) > 0 and len(ready_conns) < wait_num:
if timeout:
remain_time = timeout - (time.time() - t1)
if remain_time <= 0:
break
# connection.wait hangs if the list is empty
new_ready_conns = connection.wait(
remain_conns, timeout=remain_time)
ready_conns.extend(new_ready_conns) # type: ignore
remain_conns = [
conn for conn in remain_conns if conn not in ready_conns]
return [workers[conns.index(con)] for con in ready_conns]
[docs] def send_action(self, action: np.ndarray) -> None:
self.parent_remote.send(["step", action])
[docs] def get_result(
self,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
obs, rew, done, info = self.parent_remote.recv()
if self.share_memory:
obs = self._decode_obs()
return obs, rew, done, info
[docs] def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
self.parent_remote.send(["seed", seed])
return self.parent_remote.recv()
[docs] def render(self, **kwargs: Any) -> Any:
self.parent_remote.send(["render", kwargs])
return self.parent_remote.recv()
[docs] def close_env(self) -> None:
try:
self.parent_remote.send(["close", None])
# mp may be deleted so it may raise AttributeError
self.parent_remote.recv()
self.process.join()
except (BrokenPipeError, EOFError, AttributeError):
pass
# ensure the subproc is terminated
self.process.terminate()